ArchRWire: Visualization extensions to ArchR

R version 4.0.2
ArchRWire version 0.2.01
Vignette for ArchR: https://www.archrproject.com/bookdown/getting-started-with-archr.html
ArchR version 1.0.1

Overview

1. Set up analysis with ArchR and ArchRWire
   a) Install ArchRWire
   
2. Load exemplary ArchRProject
   
3. Cell QC
   
4. Visualization as UMAP
   a) Visualize QC parameters on single-cell embedding
   b) Visualize multiple single-cell embeddings
   c) Visualize groups of single cells in different plots (UMAP splits)
   d) Visualize clustering of single cells
   

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1. Set up analysis with ArchR and ArchRWire

### Load libraries

library(magrittr)
library(ArchR)
library(ggpubr)
library(purrr)
library(devtools)

### Set ArchR parameters.
addArchRThreads(threads = 15) # number of parallel threads
addArchRGenome('hg38') # genome and gene annotation ('hg19', 'hg38', 'mm9', 'mm10')

a) Install ArchRWire

setwd('/media/ag-rippe/isabelle/ArchRWire_dev')
with_libpaths(new = "/path/to/your/R4.0.2_libs", install('ArchRWire'))

library(ArchRWire, lib.loc = "/path/to/your/R4.0.2_libs")

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2. Load ArchRProject

overview

### Define parameters for analysis
conditions <- c('HUVEC_0min', 'HUVEC_TNFa_30min', 'HUVEC_TNFa_240min', 'HUVEC_TGFb_30min', 'HUVEC_TGFb_240min')
pal <- c('HUVEC_0min' = '#9E9E9E', 'HUVEC_TNFa_30min' = '#03D000', 'HUVEC_TNFa_240min' = '#027A00', 'HUVEC_TGFb_30min' = '#FFA91F', 'HUVEC_TGFb_240min' = '#CC7E00')

### Load ArchRProject
proj <- loadArchRProject('/media/ag-rippe/markus_side_projects/scATAC_HUVEC_TNFa_TGFb/analysis/isabelle/scATAC_10xGenomics/4_archr/ArchRProject_1_basics')

proj # Exemplary ArchRProject contains 5 samples.

           ___      .______        ______  __    __  .______      
          /   \     |   _  \      /      ||  |  |  | |   _  \     
         /  ^  \    |  |_)  |    |  ,----'|  |__|  | |  |_)  |    
        /  /_\  \   |      /     |  |     |   __   | |      /     
       /  _____  \  |  |\  \\___ |  `----.|  |  |  | |  |\  \\___.
      /__/     \__\ | _| `._____| \______||__|  |__| | _| `._____|
    

class: ArchRProject 
outputDirectory: /media/ag-rippe/markus_side_projects/scATAC_HUVEC_TNFa_TGFb/analysis/isabelle/scATAC_10xGenomics/4_archr/ArchRProject_1_basics 
samples(5): HUVEC_0min HUVEC_TNFa_30min HUVEC_TNFa_240min
  HUVEC_TGFb_30min HUVEC_TGFb_240min
sampleColData names(1): ArrowFiles
cellColData names(15): Sample TSSEnrichment ... DoubletEnrichment
  BlacklistRatio
numberOfCells(1): 23773
medianTSS(1): 9.694
medianFrags(1): 27104

proj@reducedDims # 7 iterative LSI reduced dimension objects are present.

List of length 7
names(7): IterativeLSI_25000varFeatures ... IterativeLSI_50000varFeatures

proj@embeddings # 7 single-cell UMAP embeddings are present.

List of length 7
names(7): UMAP_25000varFeatures ... UMAP_50000varFeatures

---

3. Cell QC

overview

options(repr.plot.width=40, repr.plot.height=10)
p <- lapply(conditions, function(sample){plotCellQuality(proj, sample, 3.9)})
ggAlignPlots(plotList=p, type = "h")

---

4. Visualization as UMAP

overview

a) Visualize QC parameters on single-cell embedding

umapQC <- plotUmapQC(ArchRProj = proj, # ArchRProj is mandatory
                     embedding = 'UMAP_25000varFeatures', # specify embedding to use (optional, defaults to first object in ArchRProj's embedding list)
                     coloringLayer = "Sample", # specify cellColData column to use as comparative coloring layer of single cells (optional, defaults to 'Sample' column)
                     title = 'QC parameters of single cells', # optional
                     coloringPalette = pal) # optionally specify colors for reference coloring of single cells

ggpubr::ggarrange(plotlist=umapQC, ncol=3, nrow=2)

b) Visualize multiple single-cell embeddings

umaps <- plotUmaps(proj, # ArchRProj is mandatory
                   embeddings = names(proj@embeddings[1:3]), # specify embeddings to use (optional, defaults to all objects in ArchRProj's embedding list)
                   coloringLayer = "Sample", # specify cellColData column to use as comparative coloring layer of single cells (optional, defaults to 'Sample' column)
                   coloringPalette = pal) # optionally specify colors for coloring of single cells

ggpubr::ggarrange(plotlist=umaps, ncol=3, nrow=1)

c) Visualize groups of single cells in different plots (UMAP splits)

umapSplit <- plotUmapSplit(proj, # ArchRProj is mandatory
                           embedding = 'UMAP_25000varFeatures', # specify embedding to use (optional, defaults to first object in ArchRProj's embedding list)
                           splitLayer = 'Sample', # specify cellColData column to use as split layer of single cells (optional, defaults to 'Sample' column)
                           selectLevels = conditions, # specify certain levels of split layer to visualize (optional, defaults to all levels in cellColData 'Sample' column)
                           coloringPalette = pal) # optionally specify colors for coloring of single cells

ggpubr::ggarrange(plotlist=umapSplit, ncol=3, nrow=2)

d) Visualize clustering of single cells

umapClusters <- plotUmapClusters(proj, # ArchRProj is mandatory
                                 embedding = 'UMAP_25000varFeatures', # specify embedding to use (optional, defaults to first object in ArchRProj's embedding list)
                                 reducedDims = 'IterativeLSI_25000varFeatures') # specify reduced dimensions to use (optional defaults to first object in ArchRProj's reduced dimensions list)

ggpubr::ggarrange(plotlist=umapClusters, ncol=3, nrow=1)

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Visualization with ArchRWire (02.03.2021 | v0.2.01)
i.lander@dkfz-heidelberg.de
Division of Chromatin Networks, DKFZ